Mid-Level Data Engineer

ECS Corporate Services, LLC

Fairfax, VA, United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

3 years minimum

Compensation

$90,000.0 - $115,000.0

Working hours

Regular working hours

Job source

www.disabledperson.com

Tech stack

Microsoft Azure Data as a Services Data Validation Extract Transform Load (ETL) Relational Databases Database Queries Python (Programming Language) Machine Learning Microsoft Software SQL Azure NumPy Performance Tuning

+14 more

Tensorflow Azure Machine Learning Azure Data Lake Systems Integration Unstructured Data Data Logging Azure Data Factory Pytorch Pandas Microsoft Fabric Scikit Learn Data Pipelines Web Api Microservices

Job description

Develop production-grade ETL workflows using Python and Microsoft-based frameworks.
Ingest, transform, and validate structured and unstructured data.
Implement schema enforcement, data validation, and quality checks.
Support Azure Data Lake Storage, Azure SQL, and Azure-based data services.
Design workflow orchestration using Azure Data Factory or Microsoft Fabric/Foundry.
Build Python-based data services using Pandas, PyTorch, TensorFlow, and related libraries.
Develop API endpoints and microservices to support analytics and ML platform interoperability.
Implement logging, monitoring, performance tuning, and operational reliability.
Collaborate with data scientists, analysts, architects, and governance teams.
Apply data governance best practices for compliance, reproducibility, and auditability.

Salary Range: $90,000-$115,000

Requirements

3+ years of experience developing or supporting advanced statistical, machine learning, or data pipeline solutions.
Proficiency in Python, including Pandas.
Strong SQL skills and experience integrating relational database sources.
Hands-on experience with Azure cloud environments.
Experience with ETL development using Python and Microsoft technologies.
Experience with data validation, schema enforcement, and quality assurance.
Familiarity with open-source data processing libraries such as NumPy, scikit-learn, PyTorch, or TensorFlow.